Syllabus
Random phenomena, probability, conditional prabability, independent random phenomena.
Random variables, their distributions and characteristics. Random vectors.
Law of large numbers. Central limit theorem.
Random sample, ordered sample, descriptive statistics.
Point estimates and confidence intervals. Consistent estimates. Best unbiased estimates. Maximum likelihood method. Bayes estimates.
Hypothesis tests, errors of the first and second kind, confidence level. Testing hypothesis about parameters of normal distributions. Tests of good agreement. Non-parametric tests. Sequential tests.
Linear regression. Correlation. Submodel tests.
Annotation
An introductory statistics course covering the basic of statistic thinking, fundamental principals of statistical methods, tests of hyphothesis and practical examples of experimental data analysis.